The subject matter disclosed herein relates to multi-energy imaging, including multi energy computed tomography (CT), and the generation of optimized monochromatic images in a multi-energy imaging context.
In X-ray based imaging systems, X-ray radiation spans a subject of interest, such as a human patient, and a portion of the radiation impacts a detector where the image data is collected. In digital X-ray systems a photo detector typically produces signals representative of the amount or intensity of radiation impacting discrete pixel regions of a detector surface. The signals may then be processed to generate an image that may be displayed for review. In computed tomography (CT) systems a detector array, including a series of detector elements, produces similar signals through various positions as a gantry is displaced around a patient.
In the images produced by such systems, contrast is created based upon the varying attenuation of the X-rays by the materials encountered as the X-rays penetrate the patient's tissue. Typically, materials having atoms with a greater atomic number will more strongly attenuate the passage of X-rays through the imaged volume. Thus, tissues such as bone may create relatively high contrast within an image compared to other tissues, such as fatty tissue. Some techniques used for X-ray based imaging use a contrast agent to artificially create contrast within an area that would typically not have relatively high contrast, such as blood vessels. The contrast agents may include one or more atoms capable of attenuating X-rays with a relatively high degree of efficiency, such as iodine. For example, in CT angiography, a contrast agent is typically injected into the patient, followed by CT imaging. The contrast agent typically perfuses through certain tissues of the patient, and the resulting CT images contain regions of enhanced contrast corresponding to the areas that are perfused with the contrast agent.
For typical single-energy X-ray based imaging, the resulting X-ray images are largely a representation of the average density of each analyzed voxel based upon the patient's attenuation of X rays emitted by the X-ray source and detected by the X-ray detector. However, for multi-energy X-ray imaging, a greater amount of imaging data may be gleaned for each pixel or voxel, such as an estimate of the type of material in each analyzed pixel or voxel. For example, in a dual-energy X-ray imaging system, X-ray spectra with two different energy distributions are employed. Higher-energy X-ray photons generally interact substantially less with patient tissue than the lower-energy X-rays. In the context of CT, in order to reconstruct multi-energy projection data, the underlying physical effects of X-ray interaction with matter are considered, namely, the Compton scattering effects and photoelectric effects, in a process known as material decomposition (MD). Using these techniques, it is possible to identify two or more constituent components in each analyzed voxel.
Thus, dual- or multi-energy imaging may offer the benefit of allowing tissue or material characterization. In certain contexts, such material decomposed images may allow for the generation of simulated monochromatic images, which depict the imaged region as it would appear if images using a single energy (keV) band, as opposed to a polychromatic spectrum of energy.
It has been observed, however, that such simulated monochromatic images vary in quality and do not necessarily have the desired image quality at those energy bands where good quality is expected. In particular, one variable that is outside the control of the person performing the scan is the variability introduced by the subject being imaged. That is, in a medical context, the patient undergoing imaging introduces variability in terms of their composition and path length that cannot be modeled or anticipated. This variability, therefore, can introduce variation in the observed signal-to-noise seen at the detector, even when the photon flux is kept constant during the scan. Further, some portion of this variability may manifest in scans obtained even at different energies, leading to correlated noise that observed at the different energies. Conversely, some portion of the noise observed at different energies will be uncorrelated. Taken together, this variability, whether due to variability introduced by the subject, drift introduced at the source, or noise effects introduced at the detector, can lead to image quality that is less than expected when generating simulated monochromatic images, even at a monochromatic energy that theoretically should yield the highest image quality.